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Journal of System Simulation

Abstract

Abstract: Aiming at the problem that the traditional lithium battery model has poor adaptive ability in SOC estimation and the local estimation accuracy of the single SOC estimation algorithm is low, online identification of model parameters with Multi-Innovation Stochastic Gradient algorithm and method of weighted online estimation of SOC was proposed. The results of lithium-ion battery model parameters online identification updated in real time, realizing lithium-ion battery model adaptive. Aiming at the estimation of SOC, a method of weighted online estimation method based on the PI (proportion and integral) regulator's OCV combined with Ah (an integral method) was proposed. Weights updated in real time according to SOC-E0 piecewise linear curve slope. The proposed method solved that the estimation error of SOC with OCV is large and Ah method is difficult to determine the initial value and cumulative error. Besides, the proposed method also solved the problem of online estimation of above two method. The feasibility of the method is demonstrated from the theoretical analysis, and the results of MATLAB simulation show that the proposed method has high estimation accuracy.

First Page

1677

Last Page

1684

CLC

TP391.9

Recommended Citation

Sun Haohao, Pan Tinglong, Wu Dinghui. Weighted Online Estimation of SOC Based on Adaptive Battery Model[J]. Journal of System Simulation, 2017, 29(8): 1677-1684.

DOI

10.16182/j.issn1004731x.joss.201708006

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